CN110580284A - Entity disambiguation method and device, computer equipment and storage medium - Google Patents
Entity disambiguation method and device, computer equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
Abstract
an entity disambiguation method, apparatus, computer device and storage medium comprising: acquiring a user target problem, and identifying a first entity in the target problem; judging whether a constructed user portrait sub-tree exists or not, and if not, determining an answer entity corresponding to the target question according to the first entity; if the user portrait sub-tree is established, establishing an entity sub-tree taking the first entity as a vertex, and comparing whether the distance between the entity sub-tree and the user portrait sub-tree is greater than a preset length; if the length is smaller than the preset length, selecting an entity which is lowest in level in the entity subtree and is closest to the user portrait subtree as an answer entity corresponding to the target question; if the length is larger than the preset length, the answer entity corresponding to the target question is determined according to the first entity, so that the entity disambiguation is effectively carried out by utilizing the points of interest and the hobbies of the mined user, the number of interaction rounds of the question-answering conversation system is reduced, and the convenience of the user in using the question-answering system is improved.
Description
Technical Field
The present invention relates to the field of task management technologies, and in particular, to an entity disambiguation method and apparatus, a computer device, and a storage medium.
Background
In a knowledge graph question-answering system, entity link is a primary module, namely a subject entity in a user question is identified and linked with a knowledge graph, and entity disambiguation is needed to determine an entity in a target problem, namely, the technology is specially used for solving the problem that the same-name entity generates ambiguity. At present, the main method for entity disambiguation depends on the similarity of character strings and is supplemented with manually extracted features and rules to give out a plurality of possible entities at one time, and certain disambiguation is made by combining the semantics of question sentences.
however, in the knowledge graph, there may be a plurality of entities with the same name, and it is difficult to determine which specific entity the user wants to ask only by semantic understanding in the question, so additional information is needed for entity disambiguation to determine the answer to the target question. In the intelligent conversation robot scene, entity disambiguation can be performed in a way of asking questions to the user, but interactive disambiguation is performed simply through attributes of the entities, the number of general interaction rounds is large, and user experience is poor.
Disclosure of Invention
the invention aims to provide an entity disambiguation method, an entity disambiguation device, a computer device and a storage medium, which are used for solving the problems in the prior art.
in order to achieve the above object, the present invention provides an entity disambiguation method, comprising the steps of:
Acquiring a target problem text input by a user terminal, and identifying a first entity in the target problem text;
judging whether a user portrait sub-tree of the user exists in a pre-constructed knowledge graph or not, wherein the user portrait sub-tree is established according to an entity contained in user information of the user;
if the user portrait sub-tree of the user is not established, determining an answer entity corresponding to a target question in the target question text according to the first entity, and simultaneously establishing the user portrait sub-tree of the user based on the target question text;
If the user portrait sub-tree of the user is established, establishing an entity sub-tree taking the first entity as a vertex in a knowledge graph, and comparing whether the distance between the entity sub-tree and the user portrait sub-tree is larger than a preset length;
If the distance between the entity subtree and the user portrait subtree is smaller than the preset length, selecting an entity which is lowest in level in the entity subtree and is closest to the user portrait subtree as an answer entity corresponding to the target question, and outputting the answer entity to the user terminal;
and if the distance between the entity subtree and the user portrait subtree is greater than the preset length, determining an answer entity corresponding to the target question according to the first entity, outputting the answer entity to the user terminal, and updating the user portrait subtree based on the target question.
preferably, the preset length is 0, and if it is determined that an overlapping node exists between the entity subtree and the user portrait subtree, an entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree is selected as an answer entity corresponding to the target question; and if the entity subtree and the user portrait subtree have no overlapped node, determining an answer entity corresponding to the target question according to the first entity, and updating the user portrait subtree based on the target question text of the user.
Preferably, if there are a plurality of entities with the lowest level in the entity subtree and the closest distance to the user portrait subtree, the node on the upper level of the lowest level is used as the answer entity corresponding to the target question.
preferably, the determining, according to the first entity, an answer entity corresponding to the target question includes the following steps:
Comparing the first entity with entities in a knowledge graph, and determining a second entity matched with the first entity: if only one group of second entities matched with the first entities exist in the knowledge graph, taking the second entities as answer entities corresponding to the target questions; and if a plurality of groups of matched second entities matched with the first entity exist in the knowledge graph, selecting a second entity with the highest importance from the plurality of groups of second entities as an answer entity corresponding to the target question.
Preferably, the greater the number of other entities linked to by the second entity in the knowledge-graph, the greater the importance of the second entity.
preferably, the first entity in the target problem is identified based on a NER model.
preferably, an entity sub-tree with the first entity as a vertex is searched based on the cypher statement function in Neo4 j.
to achieve the above object, the present invention further provides an entity disambiguation apparatus comprising:
the identification module is used for acquiring a target problem text input by a user terminal and identifying a first entity in the target problem text;
The user portrait sub-tree determining module is used for judging whether a user portrait sub-tree of the user exists in a pre-constructed knowledge graph or not, wherein the user portrait sub-tree is established according to an entity contained in user information of the user;
the processing module is used for determining an answer entity corresponding to the target question in the target question text according to the user portrait sub-tree, and comprises:
the first processing unit is used for determining an answer entity corresponding to the target question according to the first entity if the user portrait sub-tree of the user is not established, and establishing the user portrait sub-tree of the user based on the target question;
A second processing unit, configured to establish an entity sub-tree using the first entity as a vertex in a knowledge graph if the user portrait sub-tree of the user is established, and compare whether a distance between the entity sub-tree and the user portrait sub-tree is greater than a preset length, including:
the first processing subunit is configured to, if the distance between the entity subtree and the user portrait subtree is smaller than a preset length, select an entity that is lowest in level in the entity subtree and closest to the user portrait subtree as an answer entity corresponding to the target question, and output the answer entity to the user terminal;
and the second processing subunit is used for determining an answer entity corresponding to the target question according to the first entity when the distance between the entity subtree and the user portrait subtree is judged to be larger than the preset length, and updating the user portrait subtree based on the current user target question.
preferably, the identification module identifies a first entity in the target problem based on a NER model.
Preferably, in the user portrait sub-tree determining module, an entity sub-tree with the first entity as a vertex is searched based on a cypher statement function in Neo4 j.
As a preferable scheme, if the preset length in the second processing sub-module is 0, the first processing unit includes:
An overlapped node judging subunit, configured to judge whether there is an overlapped node between the entity subtree and the user portrait subtree;
The first answering entity determining subunit is used for selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as the answering entity corresponding to the target question when judging that the entity subtree and the user portrait subtree have the overlapped node; and when judging that the entity subtree and the user portrait subtree have no overlapped node, determining an answer entity corresponding to the target question according to the first entity, and updating the user portrait subtree based on the target question text of the user.
Preferably, in the first answering entity determining subunit, if there are a plurality of entities in the entity subtree with the lowest hierarchy and the closest distance to the user portrait subtree, the node at the upper level of the lowest hierarchy is used as the answering entity corresponding to the target question.
As a preferable scheme, the first processing module and the second processing unit respectively include:
a second entity determining subunit, configured to compare the first entity with an entity in a knowledge graph, and determine a second entity matching the first entity:
A second answer entity determination subunit, configured to, when only one group of second entities matching the first entity in the knowledge graph is determined, use the second entity as an answer entity corresponding to the target question; and when a plurality of groups of matched second entities matched with the first entity in the knowledge graph are judged, selecting the second entity with the highest importance from the plurality of groups of second entities as an answer entity corresponding to the target question.
Further, the more other entities linked to by the second entity in the knowledge-graph in the subunit are determined by the second answer entity, the higher the importance of the second entity is.
to achieve the above object, the present invention further provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above method.
The entity disambiguation method, the device, the computer equipment and the storage medium provided by the invention are implanted in a knowledge-graph question-answer dialog system, user image subtrees of the user are continuously updated according to the use of the user so as to effectively mine the attention points and the preference of the user, the disambiguation process is simplified according to the distance between the user image subtrees and the entity subtrees related to the user target problem, when the distance between the entity subtrees related to the user target problem at this time and the user image subtrees is judged to be less than the preset length, the entity with the lowest level in the entity subtrees and the closest distance to the user image subtrees is selected as the answer entity of the user target problem at this time, so that the attention points and the preference of the mined user are effectively utilized to perform entity disambiguation the entity, the condition that the user repeatedly inputs provided information for many times is avoided, and the number of interaction rounds of the question-answer dialog system, the convenience of the user in using the question answering system is improved.
drawings
FIG. 1 is a flow chart of a first embodiment of a method for entity disambiguation according to the present invention;
FIG. 2 is a block diagram of a program module of a physical disambiguation apparatus according to a first embodiment of the present invention;
Fig. 3 is a schematic diagram of a hardware structure of a first embodiment of the entity disambiguation apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, an entity disambiguation method of the present embodiment includes the following steps:
S10, acquiring a target question text input by a user terminal, and identifying a first entity in the target question text;
S20, judging whether a user portrait sub-tree of the user exists in a pre-constructed knowledge graph, wherein the user portrait sub-tree is established according to an entity contained in user information of the user, and the user portrait sub-tree is updated along with the updating of the user information;
S30, if the user portrait sub-tree of the user is not established, determining an answer entity corresponding to the target question text according to the first entity, and establishing the user portrait sub-tree of the user based on the target question text;
S40, if the user portrait sub-tree of the user is established, establishing an entity sub-tree taking the first entity as a vertex in a knowledge graph, and comparing whether the distance between the entity sub-tree and the user portrait sub-tree is larger than a preset length;
S41, if the distance between the entity subtree and the user portrait subtree is smaller than the preset length, selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as an answer entity corresponding to the target question in the target question, and outputting the answer entity to the user terminal;
s42, if the distance between the entity subtree and the user portrait subtree is larger than the preset length, determining an answer entity corresponding to a target question in the target question text according to the first entity, outputting the answer entity to the user terminal, and updating the user portrait subtree based on the target question text.
The entity disambiguation method disclosed by the application is implanted in a knowledge-graph question-answer dialog system, continuously updates user portrait subtrees according to the use of users, so as to effectively mine the attention points and the preference of the user, simplify the disambiguation process according to the distance between the user portrait sub-tree and the entity sub-tree related in the user target problem, when the distance between the entity subtree related to the current target problem of the user and the user portrait subtree is judged to be less than the preset length, selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as the answer entity of the target question of the user, thereby effectively utilizing the points of interest and the preferences of the mined users to disambiguate the entities, avoiding the situation that the users repeatedly input the provided information for many times, the number of interaction rounds of the question-answering conversation system is reduced, and the convenience of the user in using the question-answering system is improved.
In step S10, an entity description (entity description) involved in the question may be determined based on the NER model, and the first entity corresponding to the target question may be determined in the knowledge graph according to the entity description based on the entity link.
In step S20, the user information, which may be one or more combinations of basic user information, point of interest user information and historical question information, is collected to identify the entity contained in the user information to construct a user image sub-tree, and the corresponding user image sub-tree is continuously updated according to the user usage, i.e. a sub-graph (i.e. a user image sub-tree) on the knowledge graph is constructed according to the entity involved in the user initial image and the entity involved in the user usage, and when a new user starts to use the question answering system service, the user image sub-tree of the user is constructed by mining the information filled in the initial registration, and meanwhile, when a new user selects type two diabetes in the option of "disease of interest" during the user usage, type two diabetes is included in the user image sub-tree of the user, to create and continuously update the user portrait subtree. If the user information of the appointed user is not collected or stored in the pre-constructed knowledge graph, the user does not establish the user portrait subtree, and if the user information of the appointed user is collected or stored, the user portrait subtree of the user is judged to exist
in step S30, if the user portrait sub-tree of the user is not established, determining an answer entity corresponding to the target question according to the first entity includes the following steps:
Comparing the first entity with the entities in the knowledge graph, and determining a second entity matched with the first entity: if only one group of second entities matched with the first entities exist in the knowledge graph, taking the second entities as answer entities corresponding to the target questions; and if the knowledge graph has a plurality of groups of matched second entities matched with the first entity, selecting the second entity with the highest importance from the plurality of groups of second entities as the answer entity corresponding to the target question, wherein the more other entities linked in the knowledge graph by the second entity, the higher the importance of the corresponding second entity.
In some exemplary embodiments of the present disclosure, the nerr model identifies the first entity as "lina", and when the second entity is the same name as the first entity, the nerr model identifies the second entity as "lina" (which may be a collection of the second entities, i.e., a collection of the first entities, a collection of the second entities, a collection of the first entities, a "lina" (which is a collection of the first entities, a "lina", and a collection of the second entities, a "lina", and a "litna", and a "a collection of the second entities, a" a litna, a collection of the second entities, a litna "may be a litna, and a collection of the second entities, a litnax, and a" may be a litz-related to calculate a number of the second entity, and a number of the second entities, a litz-related to each of the second entity, a litna-related to each of.
In step S40, in the knowledge graph database, an entity subtree with an entity as a vertex is searched based on the cypher statement function in Neo4j, and the distance between the entity subtree and the user portrait subtree is calculated. And then comparing the distance between the two with the preset length.
In step S41, if the distance between the two entities is less than the preset length, selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as the answer entity for the question asked by the user this time; and if a plurality of entities which are lowest in the entity subtree and closest to the user portrait subtree are provided, using the upper node of the lowest level as the answer entity corresponding to the target question.
for example, when a user asks 'what fruits can be eaten in summer', the system judges that a user portrait sub-tree exists in the user, finds an entity sub-tree which takes 'fruits' as a vertex in a user question in a knowledge graph, knows that the user is a diabetic patient through the user portrait sub-tree, calculates the distance between the entity sub-tree and the user portrait sub-tree to be 0, selects an entity which is the lowest level in the entity sub-tree and is closest to the user portrait sub-tree as an entity for the user to answer at this time, and does not need to further ask the user whether the user wants to eat high-sugar fruits or low-sugar fruits, so that the experience of the user when using the question-answering system is effectively improved.
Regarding the preset length value, a smaller number is generally adopted, and considering that the preset length value is smaller, the disambiguation steps are more, but more elaborate; in this embodiment, a compromise is taken between the preset length and the preset length, and the preset length may be 1, 2, or 0. When the preset length value is 0, indicating that an entity subtree and a user portrait subtree have overlapped nodes, selecting an entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as an answer entity corresponding to the target question; and if the entity subtree and the user portrait subtree have no overlapped node, determining an answer entity corresponding to the target question according to the first entity, and updating the user portrait subtree based on the target question of the user.
In step S42, if the entity subtree and the user portrait subtree are longer than the predetermined length, the answer entity corresponding to the target question is also determined directly according to the first entity, and the user portrait subtree is updated based on the current user target question.
As mentioned above, determining an answer entity corresponding to the target question according to the first entity includes the following steps: comparing the first entity with the entities in the knowledge graph, and determining a second entity matched with the first entity: if only one group of second entities matched with the first entities exist in the knowledge graph, taking the second entities as answer entities corresponding to the target questions; and if the knowledge graph has a plurality of groups of matched second entities matched with the first entity, selecting the second entity with the highest importance from the plurality of groups of second entities as the answer entity corresponding to the target question, wherein the more other entities linked in the knowledge graph by the second entity, the higher the importance of the corresponding second entity.
the entity disambiguation method disclosed by the application is implanted in a knowledge-graph question-answer dialog system, continuously updates user portrait subtrees according to the use of users, so as to effectively mine the attention points and the preference of the user, simplify the disambiguation process according to the distance between the user portrait sub-tree and the entity sub-tree related in the user target problem, when the distance between the entity subtree related to the current target problem of the user and the user portrait subtree is judged to be less than the preset length, selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as the answer entity of the target question of the user, thereby effectively utilizing the points of interest and the preferences of the mined users to disambiguate the entities, avoiding the situation that the users repeatedly input the provided information for many times, the number of interaction rounds of the question-answering conversation system is reduced, and the convenience of the user in using the question-answering system is improved.
Example two
Continuing to refer to fig. 2, the present invention shows an entity disambiguation apparatus, in this embodiment, the entity disambiguation apparatus 10 may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to implement the present invention and implement the entity disambiguation method. The program modules referred to herein are representative of a series of computer program instruction segments capable of performing particular functions and are more suitable than the program itself for describing the execution of the entity disambiguation apparatus 10 on a storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:
An entity disambiguation apparatus comprising:
The identification module 11 is configured to obtain a target question text input by a user terminal, and identify a first entity in the target question text;
the user portrait sub-tree determining module 12 is configured to determine whether a user portrait sub-tree of the user exists in a pre-constructed knowledge graph, where the user portrait sub-tree is established according to an entity included in user information of the user;
a processing module 13, configured to determine an answer entity corresponding to the target question according to the user portrait sub-tree, where the processing module includes:
The first processing unit is used for determining an answer entity corresponding to the target question according to the first entity if the user portrait sub-tree of the user is not established, and establishing the user portrait sub-tree of the user based on the target question;
a second processing unit, configured to establish an entity sub-tree using the first entity as a vertex in a knowledge graph if the user portrait sub-tree of the user is established, and compare whether a distance between the entity sub-tree and the user portrait sub-tree is greater than a preset length, including:
the first processing subunit is configured to, if the distance between the entity subtree and the user portrait subtree is smaller than a preset length, select an entity that is lowest in level in the entity subtree and closest to the user portrait subtree as an answer entity corresponding to the target question, and output the answer entity to the user terminal;
and the second processing subunit is used for determining an answer entity corresponding to the target question according to the first entity when the distance between the entity subtree and the user portrait subtree is judged to be larger than the preset length, and updating the user portrait subtree based on the current user target question.
preferably, in the identification module 11, the first entity in the target problem is identified based on the NER model.
preferably, in the user portrait sub-tree determining module 12, an entity sub-tree with the first entity as a vertex is searched based on a cypher statement function in Neo4 j.
As a preferable scheme, if the preset length in the second processing sub-module is 0, the first processing unit includes:
An overlapped node judging subunit, configured to judge whether there is an overlapped node between the entity subtree and the user portrait subtree;
the first answering entity determining subunit is used for selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as the answering entity corresponding to the target question when judging that the entity subtree and the user portrait subtree have the overlapped node; and when judging that the entity subtree and the user portrait subtree have no overlapped node, determining an answer entity corresponding to the target question according to the first entity, and updating the user portrait subtree based on the current user target question.
Preferably, in the first answering entity determining subunit, if there are a plurality of entities in the entity subtree with the lowest hierarchy and the closest distance to the user portrait subtree, the node at the upper level of the lowest hierarchy is used as the answering entity corresponding to the target question.
As a preferable scheme, the first processing module and the second processing unit respectively include:
a second entity determining subunit, configured to compare the first entity with an entity in a knowledge graph, and determine a second entity matching the first entity:
a second answer entity determination subunit, configured to, when only one group of second entities matching the first entity in the knowledge graph is determined, use the second entity as an answer entity corresponding to the target question; and when a plurality of groups of matched second entities matched with the first entity in the knowledge graph are judged, selecting the second entity with the highest importance from the plurality of groups of second entities as an answer entity corresponding to the target question.
further, the more other entities linked to by the second entity in the knowledge-graph in the subunit are determined by the second answer entity, the higher the importance of the second entity is.
The entity disambiguating apparatus 10 of the present application is embedded in a knowledge-graph question-answering dialogue system, and continuously updates the user portrait sub-tree according to the user's usage, so as to effectively mine the attention points and the preference of the user, simplify the disambiguation process according to the distance between the user portrait sub-tree and the entity sub-tree related in the user target problem, when the distance between the entity subtree related to the current target problem of the user and the user portrait subtree is judged to be less than the preset length, selecting the entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as the answer entity of the target question of the user, thereby effectively utilizing the points of interest and the preferences of the mined users to disambiguate the entities, avoiding the situation that the users repeatedly input the provided information for many times, the number of interaction rounds of the question-answering conversation system is reduced, and the convenience of the user in using the question-answering system is improved.
EXAMPLE III
The present invention also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers) capable of executing programs, and the like. The computer device 20 of the present embodiment includes at least, but is not limited to: a memory 21, a processor 22, which may be communicatively coupled to each other via a system bus, as shown in FIG. 3. It is noted that fig. 3 only shows the computer device 20 with components 21-22, but it is to be understood that not all shown components are required to be implemented, and that more or fewer components may be implemented instead.
In the present embodiment, the memory 21 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 20, such as a hard disk or a memory of the computer device 20. In other embodiments, the memory 21 may also be an external storage device of the computer device 20, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 20. Of course, the memory 21 may also include both internal and external storage devices of the computer device 20. In this embodiment, the memory 21 is generally used for storing an operating system and various application software installed on the computer device 20, such as the program code of the entity disambiguation apparatus 10 of the first embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 20. In this embodiment, the processor 22 is configured to execute the program code stored in the memory 21 or process data, for example, execute the entity disambiguation apparatus 10 to implement the entity disambiguation method of the first embodiment.
Example four
The present invention also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the embodiment is used for storing the entity disambiguation apparatus 10, and when being executed by a processor, the entity disambiguation method of the first embodiment is implemented.
the above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
the above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. an entity disambiguation method, comprising:
Acquiring a target problem text input by a user terminal, and identifying a first entity in the target problem text;
Judging whether a user portrait sub-tree of the user exists in a pre-constructed knowledge graph or not, wherein the user portrait sub-tree is established according to an entity contained in user information of the user;
If the user portrait sub-tree of the user is not established, determining an answer entity corresponding to the target question text according to the first entity, and simultaneously establishing the user portrait sub-tree of the user based on the target question text;
If the user portrait sub-tree of the user is established, establishing an entity sub-tree taking the first entity as a vertex in a knowledge graph, and comparing whether the distance between the entity sub-tree and the user portrait sub-tree is larger than a preset length;
If the distance between the entity subtree and the user portrait subtree is smaller than the preset length, selecting an entity which is lowest in level in the entity subtree and is closest to the user portrait subtree as an answer entity corresponding to a target question in the target question, and outputting the answer entity to the user terminal;
and if the distance between the entity subtree and the user portrait subtree is greater than the preset length, determining an answer entity corresponding to a target question in the target question text according to the first entity, outputting the answer entity to the user terminal, and updating the user portrait subtree based on the target question text.
2. the entity disambiguation method of claim 1, characterized in that: if the preset length is 0, selecting an entity with the lowest level in the entity subtree and the closest distance to the user portrait subtree as an answer entity corresponding to the target question if the entity subtree and the user portrait subtree are judged to have an overlapped node; and if the entity subtree and the user portrait subtree are judged to have no overlapped node, determining an answer entity corresponding to the target question according to the first entity, and updating the user portrait subtree according to the target question text.
3. The entity disambiguation method of claim 1 or 2, characterized in that: and if a plurality of entities which are lowest in the level of the entity subtrees and are closest to the user portrait subtrees exist, using the upper level node of the lowest level as an answer entity corresponding to the target question.
4. The entity disambiguation method according to claim 1 or 2, wherein said determining, from said first entity, an answer entity to which said target question corresponds comprises the steps of:
Comparing the first entity with a second entity in a knowledge graph, and determining the second entity matched with the first entity: if only one group of second entities matched with the first entities exist in the knowledge graph, taking the second entities as answer entities corresponding to the target questions; and if a plurality of groups of matched second entities matched with the first entity exist in the knowledge graph, selecting a second entity with the highest importance from the plurality of groups of second entities as an answer entity corresponding to the target question.
5. the entity disambiguation method of claim 4, wherein the greater the number of other entities linked in the knowledge-graph by the second entity, the greater the importance of the second entity.
6. the entity disambiguation method of claim 1, characterized in that the first entity in the target question text is identified based on a NER model.
7. The entity disambiguation method of claim 1 wherein the entity subtree that is topped by the first entity is located based on a cypher statement function in Neo4 j.
8. an entity disambiguation apparatus, comprising:
the identification module is used for acquiring a target problem text input by a user terminal and identifying a first entity in the target problem text;
The user portrait sub-tree determining module is used for judging whether a user portrait sub-tree of the user exists in a pre-constructed knowledge graph or not, wherein the user portrait sub-tree is established according to an entity contained in user information of the user;
The processing module is used for determining an answer entity corresponding to the target question in the target question text according to the user portrait sub-tree, and comprises:
the first processing unit is used for determining an answer entity corresponding to the target question according to the first entity if the user portrait sub-tree of the user is not established, and establishing the user portrait sub-tree of the user based on the target question text;
a second processing unit, configured to establish an entity sub-tree using the first entity as a vertex in a knowledge graph if the user portrait sub-tree of the user is established, and compare whether a distance between the entity sub-tree and the user portrait sub-tree is greater than a preset length, including:
The first processing subunit is configured to, if the distance between the entity subtree and the user portrait subtree is smaller than a preset length, select an entity that is lowest in level in the entity subtree and closest to the user portrait subtree as an answer entity corresponding to the target question, and output the answer entity to the user terminal;
and the second processing subunit is used for determining an answer entity corresponding to the target question according to the first entity when the distance between the entity subtree and the user portrait subtree is judged to be larger than the preset length, and updating the user portrait subtree based on the target question text of the user.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented by the processor when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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